Comparison of sensor selection mechanisms for an ERP-based brain-computer interface.

A major barrier for a broad applicability of brain-computer interfaces (BCIs) based on electroencephalography (EEG) is the large number of EEG sensor electrodes typically used. The necessity for this results from the fact that the relevant information for the BCI is often spread over the scalp in complex patterns that differ depending on subjects and application scenarios. Recently, a number of methods have been proposed to determine an individual optimal sensor selection. These methods have, however, rarely been compared against each other or against any type of baseline. In this paper, we review several selection approaches and propose one additional selection criterion based on the evaluation of the performance of a BCI system using a reduced set of sensors. We evaluate the methods in the context of a passive BCI system that is designed to detect a P300 event-related potential and compare the performance of the methods against randomly generated sensor constellations. For a realistic estimation of the reduced system's performance we transfer sensor constellations found on one experimental session to a different session for evaluation. We identified notable (and unanticipated) differences among the methods and could demonstrate that the best method in our setup is able to reduce the required number of sensors considerably. Though our application focuses on EEG data, all presented algorithms and evaluation schemes can be transferred to any binary classification task on sensor arrays.

Combining functional and anatomical connectivity reveals brain networks for auditory language comprehension.

Cognitive functions are organized in distributed, overlapping, and interacting brain networks. Investigation of those large-scale brain networks is a major task in neuroimaging research. Here, we introduce a novel combination of functional and anatomical connectivity to study the network topology subserving a cognitive function of interest. (i) In a given network, direct interactions between network nodes are identified by analyzing functional MRI time series with the multivariate method of directed partial correlation (dPC). This method provides important improvements over shortcomings that are typical for ordinary (partial) correlation techniques. (ii) For directly interacting pairs of nodes, a region-to-region probabilistic fiber tracking on diffusion tensor imaging data is performed to identify the most probable anatomical white matter fiber tracts mediating the functional interactions. This combined approach is applied to the language domain to investigate the network topology of two levels of auditory comprehension: lower-level speech perception (i.e., phonological processing) and higher-level speech recognition (i.e., semantic processing). For both processing levels, dPC analyses revealed the functional network topology and identified central network nodes by the number of direct interactions with other nodes. Tractography showed that these interactions are mediated by distinct ventral (via the extreme capsule) and dorsal (via the arcuate/superior longitudinal fascicle fiber system) long- and short-distance association tracts as well as commissural fibers. Our findings demonstrate how both processing routines are segregated in the brain on a large-scale network level. Combining dPC with probabilistic tractography is a promising approach to unveil how cognitive functions emerge through interaction of functionally interacting and anatomically interconnected brain regions.